hfarahat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 8.3.0\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "import json\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "import bson\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "import pymongo\n",
    "print (\"IPython version:      %6.6s\", IPython.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[ 0.20354485  0.87353642  0.79226415]\n",
      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
      "Add 5 to every element\n",
      "[[ 5.20354485  5.87353642  5.79226415]\n",
      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
      "Get the first row\n",
      "[ 5.20354485  5.87353642  5.79226415]\n"
     ]
    }
   ],
   "source": [
    "#Here is what numpy can do\\n\",\n",
    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
    "x = np.random.random((2, 3))\n",
    "print (x)\n",
    "\n",
    "#array operation (as in R)\n",
    "print (\"Add 5 to every element\")\n",
    "x = x + 5\n",
    "print (x)\n",
    "\n",
    "# get a slice (first row) (as in R)\n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
    "#       on possible completions.\n",
    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
    "x.any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aIPsbgIu69+lFwOXj3P4RX/RJnsbg/4CrquqGofHXAK8ELqju5BkHcWuHw+lA2YdcBfxB\ntzzt2Z/H4HzqV5Ls6vLdleQXmf7sVNUTVfWjqvox8Nc8dapgarIfYH/ZDdzQnSr4EvBjBveNmZrc\nsOj7dBXw+8C1Q9NXQvZNwJPLf8+495dJXKCYli8GFzyuBD603/gZDG6pPLPf+Av5yYs8DzO5izwH\nyn7S0PKbgeu75bP5yYs8X5q2132/Obt46mLs1GcH1gwtX8TgHPHU7DOL5H498Kfd8gsYnDbItORe\nan/p3qtf2G9s6rMzOFf/sm75dODObnks+/ph/x8+TV/AbzG4KHIPcHf3dRaDizePDo19fOg5lzC4\niv8g3VXzKcv+KeDebvwfGFygfXKH+2iX/avA7LRl32/OcNFPfXbgb7ts9zC4r9Nw8U98n1kk99HA\n33X7zF3Ay6cp91L7C/BJ4PULPGeqs3fjdzL4hbQD+PVu/lj2df8yVpIad8Sfo5ek1ln0ktQ4i16S\nGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8ApNqW0K2lWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2433ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "heads = np.random.binomial(500, .5, size=500)\n",
    "histogram = plt.hist(heads, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Task 1\n",
    "## write a program to produce Fibonacci numbers up to 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n",
      "13\n",
      "21\n",
      "34\n",
      "55\n",
      "89\n",
      "144\n",
      "233\n",
      "377\n",
      "610\n",
      "987\n",
      "1597\n",
      "2584\n",
      "4181\n",
      "6765\n",
      "10946\n",
      "17711\n",
      "28657\n",
      "46368\n",
      "75025\n",
      "121393\n",
      "196418\n",
      "317811\n",
      "514229\n",
      "832040\n",
      "1346269\n"
     ]
    }
   ],
   "source": [
    "a = 0\n",
    "b = 1\n",
    "c = 1\n",
    "print (a)\n",
    "print (b)\n",
    "while (c <= 1000000):\n",
    "    c = a + b\n",
    "    print (c)\n",
    "    a = b\n",
    "    b = c\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
    "## Calculate the mean and standard deviation of that sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 0.489\n",
      "Standard deviation =  0.49987898535545583\n"
     ]
    }
   ],
   "source": [
    "n = 1        # number of tosses\n",
    "p = 0.5      # probability of head/tail (fair coin, 0.5 each)\n",
    "size = 1000  # repeat the toss for 1000 times\n",
    "s = np.random.binomial(n, p, size) # generate the output array\n",
    "#print (s)\n",
    "m = np.mean(s) # calculate mean\n",
    "print ('mean =',m )\n",
    "st = np.std(s) # calculate standard deviation\n",
    "print ('Standard deviation = ',st)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3\n",
    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
    "### use numpy.random.normal to generate gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f52a53ecfd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.random.normal(0,5,100)\n",
    "#print (x)\n",
    "e = np.random.normal(0,1,100)\n",
    "#print (e)\n",
    "y = 0.5 * x + e\n",
    "\n",
    "plt.scatter(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}